摘要
针对强跟踪奇异值分解(Singular Value Decomposition,SVD)和无迹卡尔曼滤波(Unscented Kalman Filter,UKF)算法在新息数据误差较大时易导致滤波精度降低的问题,文中提出一种基于M估计的强跟踪SVD-UKF算法。该算法利用M估计理论,对异常新息数据进行"筛选",保留有用新息,剔除有害新息,有效避免由卫星信号野值引起的粗差对强跟踪SVD-UKF算法的鲁棒性影响。将该算法应用于组合导航系统仿真实验,结果证明了本文方法有效的提高了系统在新息数据异常时的鲁棒性。
The performance of strong tracking SVD-UKF algorithm,it is easy to reduce the filtering accuracy when the error of the new data is large,a Improved strong tracking SVD-UKF algorithm was proposed. This algorithm uses the M estimation theory,the abnormal data of new information ＂ screening＂,retaining the useful new information,eliminating harmful innovation,effectively avoid the satellite signal outliers caused by outliers to influence the robustness of strong tracking algorithm SVD-UKF. The algorithm is applied to the simulation of the integrated navigation system,and the results show that the proposed method is effective and can improve the robustness of the system when the new data is abnormal.
作者
池传国
黄国勇
孙磊
CHI Chuanguo;HUANG Guoyong;SUN Lei(School of Information Engineering ＆ Automation,Kunming University of Science and Technology,Kunming 650500,China;Engineering Research Center for Mineral Pipeline Transportation.YN,Kunming 650500,China)
出处
《电子科技》
2018年第7期42-45,54,共5页
Electronic Science and Technology
基金
国家自然科学基金(51169007)
云南省科技计划项目(2013CA022
2012DA005
2011DH034)
云南省中青年学术和技术带头人后备人才培养计划项目(2011CI017)
